The scaling laws are likely to continue, but the details of how AI will evolve are unpredictable and could lead to unexpected outcomes.

If the old scaling laws are beginning to lose their edge, we need to consider what comes next for AI development.

There is an ongoing debate in the AI community about whether scaling laws will continue to hold or if we are approaching bottlenecks in model performance.

The scaling laws in AI are still valid, but as we scale, challenges such as data management and system complexity become more pronounced, necessitating innovative solutions.

If scaling plateaus before we reach human-level intelligence, it could be due to running out of data or compute resources, but I don't think that's likely.

There is ongoing debate in the AI community about whether we have reached the limits of scaling laws, as the latest models have become larger and more expensive, yet their capabilities seem to be plateauing.

We had to be very paranoid about scaling laws, ensuring that any wins from new features were persistent across different scales.

I think the truth is that we still don't know why scaling works in AI. It's almost entirely an empirical fact, and we don't have a satisfying explanation for it yet.

The first problem with our approach to building large-scale computers is that Moore's Law is ending, which has been happening for a long time.